Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To analyze the key factors,hierarchical structure and internal action paths that affect the ability improvement of medical insurance management personnel in public hospitals,and to provide theoretical basis and practical reference for strengthening the construction of medical insurance management talent teams in hospitals.Methods:Through the mutual verification of literature analysis,policy interpretation and interview results,an index system of influencing factors for the ability improvement of medical insurance management personnel in public hospitals was constructed.A hybrid method combining DEMATEL-ISM-MICMAC was adopted to define the relationships,divide the levels and conduct driving force-dependence analysis of the influencing factors.Results:There are a total of 12 key factors influencing the ability improvement of medical insurance management personnel in public hospitals.Through the ISM model,these influencing factors can be classified into four levels:surface factors,intermediate factors,deep factors,and essential factors.With the help of MICMAC analysis,it can be classified into the spontaneous factor group of"low driving force-low dependence",the independent factor group of"high driving force-low dependence"and the dependent factor group of"low driving force-high dependence".Conclusion:Policy interpretation and knowledge reserve are the fundamental driving factors for ability improvement;Professional ethics and responsibilities are the deep-seated supporting factors of the ability system.The regulatory capacity of medical insurance funds is a key outcome and performance manifestation.

Objective To elucidate the impact of chronic kidney disease(CKD)on the clinical outcomes of patients with left main coronary artery disease(LMCAD)undergoing intravascular ultrasound(IVUS)-guided percutaneous coronary intervention(PCI).Methods This retrospective study enrolled consecutive patients with LMCAD who underwent IVUS-guided PCI at Wuhan Asia Heart Hospital between January 2017 and December 2020.Patients were stratified into CKD and non-CKD groups according to the presence of CKD.Clinical data were systematically retrieved from the electronic health record system.Demographic,clinical,and angiographic characteristics were compared between groups.The primary endpoint was major adverse cardiovascular events(MACE),defined as a composite of all-cause mortality,myocardial infarction,and ischemic stroke.Results A total of 325 LMCAD patients[mean age(62.56±9.86)years;73.54％male]were included,with 31 patients(9.54％)in the CKD group.During a median follow-up of 5 years,CKD patients exhibited significantly older age[(70.13±9.77)years vs.(61.77±9.54)years,P＜0.001],higher prevalence of three-vessel disease(64.52％vs.38.10％;P=0.040)and left main bifurcation lesion(45.16％vs.37.76％,P=0.011),greater IVUS-detected calcification burden(P=0.029),and higher median SYNTAXⅡ scores[(34.10(30.30,39.25)vs.26.75(22.42,31.58),P＜0.001)].The cumulative incidence of MACE was significantly higher in the CKD group compared to the non-CKD group(32.26％vs.9.18％,P＜0.001).Univariate Cox regression analysis and Kaplan-Meier survival curves confirmed a 5.877-fold increased risk of MACE in CKD patients(95％CI 2.765-12.494).After adjusting for age and cardiac function,CKD remained an independent predictor of MACE(HR 3.611,95％CI 1.634-7.978).Conclusions LMCAD patients with concomitant CKD present with advanced age,impaired cardiac function,more extensive coronary disease,and severe calcification.The presence of CKD is associated with a significantly worse long-term prognosis.

Diabetes-related cognitive impairment(DCI)is a major complication of type 2 diabetes melli-tus(T2DM).Although exercise is essential in alleviating DCI,the underlying mechanisms remain un-clear.The aim of this study is to investigate the role and mechanism of exosomal miR-126a-5p induced by exercise in ameliorating DCI.Twenty-four 16-week-old male db/db mice were randomly divided into dia-betes group(n=12;DM)and exercise intervention group(n=12;DE).The control group consisted of male m/m mice of the same age group(n=12;CON).The DE group underwent 8 weeks of moderate in-tensity treadmill training(10 m/min,5 days a week).In the MWM experiment,compared to the CON group,the DM group exhibited prolonged escape latency(P<0.01),reduced swimming speed and target quadrant time(P<0.001),and decreased expression of miR-126a-5p and EX-miR-126a-5p in hipp-ocampal tissue(P<0.001).After exercise intervention,the DE group showed improved performance with decreased escape latency(P<0.05),increased swimming speed and target quadrant time(P<0.05),and elevated levels of exosomal miR-126a-5p(P<0.001).Morphological staining revealed a de-crease in the expression and proportion of NeuN in hippocampal neurons and an increase in the expression and proportion of glial cells in the CA1 and CA3 regions of DM group mice compared to CON group mice(P<0.05),while DE group mice showed increased fluorescence intensity and proportion of neurons(P<0.05).Western blotting analysis revealed that the DM group also showed significant upregulation of amy-loid β(Aβ),high mobility group box 1(Hmgb1),and NF-κB in the hippocampus(P<0.05),which were reduced after exercise(P<0.05).Moreover,exosomal miR-126a-5p overexpression greatly de-creased the levels of Hmgb1,NF-κB,and amyloid precursor protein(APP)in HT22 cells and TNF-α,IL-1β in supernatant exposed to HG(P<0.05),while inhibition of miR-126a-5p led to increased levels of these proteins(P<0.05).In conclusion,eight weeks of treadmill exercise improved cognitive function in db/db mice,likely through the EXs-miR-126/HMGB1/NF-κB pathway to reduce inflammation in hip-pocampal tissue.

High mobility group box protein 1(HMGB1)is one of the most widely expressed protein member in the HMGs family,which is well known for its involvement in the body inflammatory response.Previous researches have found that it plays a significant role in cell migration,immune identification and neuroprotection.Spinal cord injury is a disease that causes severe damage to the nervous system,and neural circuits are disrupted after a spinal cord injury,which leads to many conditions including ischemia and hypoxia,inflammatory responses,demyelinating lesions,and glial scar formation that are detrimental to nerve regeneration and repair,making it one of the most difficult diseases to treat in the modern spinal surgery field.HMGB1 is upregulated after spinal cord injury,thereby regulating neuroinflam-matory responses,and participating in the neuronal apoptosis,promoting neuronal regeneration,and inducing neural stem cell differentiation and migration,which plays an important role in the process of neural function recovery.This paper summarizes the structure and function of HMGB1,as well as its role in spinal cord injury,in order to provide direction for founding therapeutic target for neurological function recovery after spinal cord injury.

Aim To investigate the effect of tetrameth-ylpyrazine(TMP)on neuroinflammation after cerebral ischemia and hypoxia via mannose-binding lectin(MBL).Methods Patients diagnosed with ischaemic stroke at Shanxi Provincial People's Hospital were in-cluded in the study,and their clinicopathological data,as well as blood and urine samples,were collected with the consent of the patients and their families.Using these biological samples,differential proteins and tar-gets were identified by proteomic analysis and subse-quently verified with animal experiments.The mice were divided into the sham,dMCAO,and TMP(10,20,40 mg·kg-1)treatment groups.After seven days of drug administration,the modified neurological sever-ity score(mNSS)was used to assess the neurological function.TTC staining was used to detect the volume of cerebral infarction.Motor function was evaluated be-haviourally,and ELISA was used to detect MASP1,sC5b-9,TNF-α,IL-6,and IL-1β.Western blot was used to determine the expression of relevant proteins,such as MBL2,MASP2,and C3.Results Compared with the sham group,the dMCAO group exhibited in-creased neurological impairment,which was signifi-cantly ameliorated by TMP treatment.The expression levels of MBL2,C3 and MASP2 were elevated in the dMCAO group and were reduced following TMP treat-ment.Additionally,the dMCAO group showed elevat-ed expression of inflammatory factors IL-1 β,IL-6 and TNF-α,which were then suppressed by TMP treat-ment.Conclusion TMP inhibits the inflammatory re-sponse after ischemia and hypoxia by regulating MBL,thus attenuating brain injury.